With the advent of the Internet, and especially electronic commerce (“e-commerce”) over the Internet, the use of data analysis tools, has increased. In e-commerce and other Internet and non-Internet applications, databases are generated and maintained that have large amounts of information. Such information can be analyzed, or “mined,” to learn additional information regarding customers, users, products, etc.
Data mining (also known as Knowledge Discovery in Databases—KDD) has been defined as “The nontrivial extraction of implicit, previously unknown, and potentially useful information from data.” It uses machine learning, statistical and visualization techniques to discover and present knowledge in a form that is easily comprehensible to humans. A—known type of data visualization technique is a dependency network. Briefly stated, a dependency network is a graphical representation of probabilistic relationships, such as may be a collection of regressions or classifications of among variables in a domain. Dependency networks are particularly useful in visualizing data because several computationally efficient algorithms exist for learning both the structure and probabilities of a dependency network from data. In addition, dependency networks are well suited to the task of predicting preferences and are generally useful for probabilistic inference.
Various other data analysis tools exist from which one may leverage the data already contained in databases to learn new insights regarding the data by uncovering useful patterns, relationships, or correlations.
It is usually desirable for a data analyst to visualize the relationships and patterns underlying the data. Existing exploratory data analysis techniques include plotting data for subsets of variables, and various clustering methods. However, inasmuch as the data analyst desires to have as many tools at his or her disposal as possible, new visualization techniques for displaying the relationships and patterns underlying data are always welcome.